Rost et al., 2000 - Google Patents
Third generation prediction of secondary structuresRost et al., 2000
- Document ID
- 11217322161301663958
- Author
- Rost B
- Sander C
- Publication year
- Publication venue
- Protein structure prediction: Methods and protocols
External Links
Snippet
The sequence-structure gap is rapidly increasing. Currently, databases for protein sequences (eg, SWISS-PROT [1]) are expanding rapidly, largely due to large-scale genome sequencing projects: at the beginning of 1998, we know already all sequences for a dozen …
- 102000004169 proteins and genes 0 abstract description 70
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/706—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for drug design with the emphasis on a therapeutic agent, e.g. ligand-biological target interactions, pharmacophore generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rost et al. | Third generation prediction of secondary structures | |
Dill et al. | Protein actions: Principles and modeling | |
Sippl | Knowledge-based potentials for proteins | |
Sultan et al. | Transferable neural networks for enhanced sampling of protein dynamics | |
Rost | Rising accuracy of protein secondary structure prediction | |
Higgins et al. | Bioinformatics: Sequence, Structure and Databanks: A Practical Approach | |
KR101239466B1 (en) | Method and device for partitioning a molecule | |
Krupa et al. | Maximum likelihood calibration of the UNRES force field for simulation of protein structure and dynamics | |
Liu et al. | Exploratory studies of ab initio protein structure prediction: multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model | |
Saven | Designing protein energy landscapes | |
Rose | Reframing the protein folding problem: Entropy as organizer | |
Oliveira Jr et al. | Distinguishing biomolecular pathways and metastable states | |
US6832162B2 (en) | Methods of ab initio prediction of α helices, β sheets, and polypeptide tertiary structures | |
Sultan et al. | Transfer learning from Markov models leads to efficient sampling of related systems | |
Rost | How to use protein 1-D structure predicted by PROFphd | |
Head-Gordon et al. | Computational challenges in structural and functional genomics | |
Bystroff et al. | Helix propensities of short peptides: molecular dynamics versus bioinformatics | |
Zou et al. | Local interactions that contribute minimal frustration determine foldability | |
Wang et al. | Predicting DNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature | |
Martin et al. | In silico local structure approach: a case study on outer membrane proteins | |
Zagrovic | Studying protein folding and dynamics using world-wide distributed computing | |
Gupta et al. | A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop | |
Yrazu et al. | The Effect of Electrostatic Interactions on the Folding Kinetics of a 3-α-Helical Bundle Protein Family | |
Rost | Columbia University, New York, New York, USA | |
Pandit | Prediction of calmodulin-binding proteins using short linear motifs |